Get Latest Aug-2022 Conduct effective penetration tests using DumpsReview AI-900 exam [Q55-Q71]

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Get Latest [Aug-2022] Conduct effective penetration tests using DumpsReview AI-900

Penetration testers simulate AI-900 exam PDF

Certification Topics of Microsoft AI-900 Exam

Our Microsoft AI-900 exam dumps covers the following objectives of Microsoft AI-900 Exam.

Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
Describe fundamental principles of machine learning on Azure (30-35%)
Describe AI workloads and considerations (15-20%)
Describe features of conversational AI workloads on Azure (15-20%)
Describe features of computer vision workloads on Azure (15-20%)

Explain Conversational Artificial Intelligence Workloads Features on Azure (15-20%)

In the last section, you will face with the following subtopics:

Identify the basic use cases for Conversational Artificial Intelligence (AI) – The students should be able to identify the features and usage for a range of elements. These include personal digital assistants, webchat bots, and telephone voice menus. It also covers the skills in identifying the basic features of conversational Artificial Intelligence solutions.
Establish Azure services for Conversational Artificial Intelligence – The potential candidates for the Microsoft AI-900 exam should have the capacity to identify the capabilities of Azure Bot Service and QnA Maker service.

NO.55 Which two scenarios are examples of a conversational AI workload? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

a smart device in the home that responds to Questions such as “What will the weather be like today?”

a website that uses a knowledge base to interactively respond to users’ Question:s

assembly line machinery that autonomously inserts headlamps into cars

monitoring the temperature of machinery to turn on a fan when the temperature reaches a specific threshold

NO.56 You are developing a chatbot solution in Azure.
Which service should you use to determine a user’s intent?

Translator Text

QnA Maker

Speech

Language Understanding (LUIS)

Language Understanding (LUIS) is a cloud-based API service that applies custom machine-learning intelligence to a user’s conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.
Design your LUIS model with categories of user intentions called intents. Each intent needs examples of user utterances. Each utterance can provide data that needs to be extracted with machine-learning entities.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis

NO.57 Match the Microsoft guiding principles for responsible AI to the appropriate descriptions.
To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Explanation
Box 1: Reliability and safety
To build trust, it’s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.
Box 2: accountability
Box 3: Privacy and security
As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

NO.58 You have a dataset that contains information about taxi journeys that occurred during a given period.
You need to train a model to predict the fare of a taxi journey.
What should you use as a feature?

the number of taxi journeys in the dataset

the trip distance of individual taxi journeys

the fare of individual taxi journeys

the trip ID of individual taxi journeys

The label is the column you want to predict. The identified Features are the inputs you give the model to predict the Label.
Example:
The provided data set contains the following columns:
rate_code: The rate type of the taxi trip is a feature.
passenger_count: The number of passengers on the trip is a feature.
trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don’t know how long the trip would take. Thus, the trip time is not a feature and you’ll exclude this column from the model.
trip_distance: The distance of the trip is a feature.
payment_type: The payment method (cash or credit card) is a feature.
fare_amount: The total taxi fare paid is the label.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices

NO.59 You need to make the press releases of your company available in a range of languages.
Which service should you use?

Translator Text

Text Analytics

Speech

Language Understanding (LUIS)

Section: Describe features of Natural Language Processing (NLP) workloads on Azure Explanation:
Translator is a cloud-based machine translation service you can use to translate text in near real-time through a simple REST API call. The service uses modern neural machine translation technology and offers statistical machine translation technology. Custom Translator is an extension of Translator, which allows you to build neural translation systems.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/translator/

NO.60 In which scenario should you use key phrase extraction?

translating a set of documents from English to German

generating captions for a video based on the audio track

identifying whether reviews of a restaurant are positive or negative

identifying which documents provide information about the same topics

NO.61 Which metric can you use to evaluate a classification model?

true positive rate

mean absolute error (MAE)

coefficient of determination (R2)

root mean squared error (RMSE)

Explanation
What does a good model look like?
An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner.
Worse than random would dip below the y=x line.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classification

NO.62 For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

NO.63 You build a machine learning model by using the automated machine learning user interface (UI).
You need to ensure that the model meets the Microsoft transparency principle for responsible AI.
What should you do?

Set Validation type to Auto.

Enable Explain best model.

Set Primary metric to accuracy.

Set Max concurrent iterations to 0.

Model Explain Ability.
Most businesses run on trust and being able to open the ML “black box” helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.
Reference:
https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/

NO.64 For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Reference:
https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

NO.65 To complete the sentence, select the appropriate option in the answer area.

Explanation

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

NO.66 You are building a chatbot that will use natural language processing (NLP) to perform the following actions based on the text input of a user:
* Accept customer orders.
* Retrieve support documents.
* Retrieve order status updates.
Which type of NLP should you use?

sentiment analysis

translation

language modeling

named entity recognition

NO.67 For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Explanation

Box 1: Yes
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
Box 2: Yes
With the designer you can connect the modules to create a pipeline draft.
As you edit a pipeline in the designer, your progress is saved as a pipeline draft.
Box 3: No
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

NO.68 Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Explanation

Box 3: Natural language processing
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

NO.69 You have the following dataset.

You plan to use the dataset to train a model that will predict the house price categories of houses.
What are Household Income and House Price Category? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/interpret-model-results

NO.70 A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain haemorrhage types.
You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person.
This is an example of which type of machine learning?

clustering

regression

classification

Reference:
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/int

NO.71 Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.